4
4 Goal Familiarize you with the state-of-art in Machine Learning Breadth: many different techniques Depth: Project Hands-on experience Develop the way of machine learning thinking Learn how to model real-world problems by machine learning techniques Learn how to deal with practical issues

17
17 Software that Models Users Description: A homicide detective and a fire marshall must stop a pair of murderers who commit videotaped crimes to become media darlings Rating: Description: Benjamin Martin is drawn into the American revolutionary war against his will when a brutal British commander kills his son. Rating: Description: A biography of sports legend, Muhammad Ali, from his early days to his days in the ring Rating: History What to Recommend? Description: A high-school boy is given the chance to write a story about an up-and-coming rock band as he accompanies it on their concert tour. Recommend: ? Description: A young adventurer named Milo Thatch joins an intrepid group of explorers to find the mysterious lost continent of Atlantis. Recommend: ? No Yes

21
21 What is the Learning Problem Learning = Improving with experience at some task Improve over task T With respect to performance measure P Based on experience E Example: Learning to Play Backgammon T: Play backgammon P: % of games won in world tournament E: opportunity to play against itself

22
22 Backgammon More than 10 20 states (boards) Best human players see only small fraction of all board during lifetime Searching is hard because of dice (branching factor > 100)

23
23 TD-Gammon by Tesauro (1995) Trained by playing with itself Now approximately equal to the best human player

24
24 Learn to Play Chess Task T: Play chess Performance P: Percent of games won in the world tournament Experience E: What experience? How shall it be represented? What exactly should be learned? What specific algorithm to learn it?

27
27 Value Function V(b): Example Definition If b final board that is won: V(b) = 1 If b final board that is lost: V(b) = -1 If b not final boardV(b) = E[V(b*)] where b* is final board after playing optimally

28
28 Representation of Target Function V(b) Same value for each board Lookup table (one entry for each board) No Learning No Generalization Summarize experience into Polynomials Neural Networks

35
35 Importants Issues in Machine Learning Obtaining experience How to obtain experience? Supervised learning vs. Unsupervised learning How many examples are enough? PAC learning theory Learning algorithms What algorithm can approximate function well, when? How does the complexity of learning algorithms impact the learning accuracy? Whether the target function is learnable? Representing inputs How to represent the inputs? How to remove the irrelevant information from the input representation? How to reduce the redundancy of the input representation?